How to apply spatial saliency into objective metrics for JPEG compressed images?

This paper investigates how saliency obtained from eye-tracking data can be integrated into objective metrics for JPEG compressed images. The objective metrics used in this paper are both based on features, locally extracted from the images and serving as input to a neural network for the overall quality prediction. We compare various weighting functions to combine saliency with these objective metrics, taking into account the possible distraction due to artifacts that might affect the quality judgment. Experimental results indicate that including saliency into objective metrics in an appropriate way can further enhance their performance.